The growth of interconnected networks of computing devices has enabled the sharing of data on a worldwide basis. To obtain such data, users typically utilize a “search engine” to search for data that is of interest to the users. The search engine utilizes the information provided by the user to identify, and then return to the user, data that is responsive to the information provided by the user. Typically, the information provided by the user is in the form of one or more terms describing the data that the user wishes to find. Likewise, the data that is identified, by the search engine, as being responsive to the user's query is, traditionally, hypertextual data comprising text, graphics, video and the like, as well as links to other data. However, in certain situations, the user wishes to find data of a specific type, such as, for example, images, videos, or files.
Because many people are adept at processing information visually, users often utilize search engines to search for images that are associated with one or more search terms. Such image-centric searches typically arise in one of two scenarios: either the user is looking for a graphic for entertainment or educational purposes, or the user is searching for a specific product that the user can identify visually.
In the first scenario, the images identified by the search engine as being responsive to the user's query can vary substantially. For example, a student searching for a picture of George Washington may be presented, by the search engine, with a wide variety of pictures including, for example, individual portraits of George Washington, paintings where George Washington is depicted with others, and images of structures, such as, for example, the Washington Monument. Similarly, as another example, a tourist searching for images associated with Paris, France may be presented, by the search engine, with a wide variety of images including, for example, images of famous Parisian landmarks, such as the Eiffel Tower, pictures taken by other tourists of themselves in Paris, and panoramic images of Paris.
By contrast, in the second scenario, where a user is searching for a specific product, the images identified by the search engine as being responsive to the user's query can be substantially more similar to one another. Typically, a user seeking to buy a particular product, or a user seeking information about a particular product, provides, to the search engine, much more specific information, thereby enabling the search engine to more precisely identify responsive images. For example, users looking for a product can provide, to the search engine, a product identification code, a trademarked name, or other detailed, and often unique, product identifying information. As a result, the images that are returned, by the search engine, are very similar to one another in that the vast majority of the returned images are of the same product and, indeed, often the very same image itself, such as a promotional image provided by a manufacturer of the product. Some of the returned images, however, can be images that are not of the product. For example, the images that were returned by the search engine can include images that were improperly associated with the product. Others of the images that were returned by the search engine can include images that were meant to be placeholders, or indicators that no image of the product is available.
Traditional mechanisms for identifying images that are different from what the user would have expected rely on human intelligence to filter out such images. Even in situations where the user searches for a precisely identified product, and the responsive images are, for the most part, very similar to one another, the mechanisms for identifying those images that are not of the product that the user was searching for still rely on a human to ultimately make the decision of whether or not a particular image should be excluded from future search results directed to the same product.